Replication of prior self-anchoring findings: Self-evaluations predicting ingroup evaluations
m <- glmer( ingChoiceN ~ selfResp.Z + ( selfResp.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
library(broom.mixed)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
m <- glmer( ingChoiceN ~ SE.Z + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
m <- glmer( ingChoiceN ~ SE.Z * novel + ( SE.Z + novel | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
m <- glmer( as.factor(ingChoiceN) ~ SE.Z + scale(desirability) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
m <- glmer( as.factor(ingChoiceN) ~ scale(oSE) + ( scale(oSE) | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
m <- glmer( as.factor(ingChoiceN) ~ scale(iSE) + ( scale(iSE) | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel) + ( SE.Z + as.factor(novel) | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel) + scale(desirability) + ( SE.Z + as.factor(novel) + scale(desirability) | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel")) %>% plot()
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(RSE) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ SE.Z * scale(RSE) + (SE.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2099.8 2142.9 -1041.9 2083.8 1596
Scaled residuals:
Min 1Q Median 3Q Max
-4.1305 -0.9859 0.5201 0.9041 1.3766
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.01490 0.1221
subID (Intercept) 0.42374 0.6510
SE.Z 0.07242 0.2691 0.61
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38812 0.20487 1.895 0.0582 .
SE.Z 0.17205 0.09906 1.737 0.0824 .
scale(RSE) 0.27966 0.20533 1.362 0.1732
SE.Z:scale(RSE) 0.09111 0.09918 0.919 0.3583
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE.Z s(RSE)
SE.Z 0.493
scale(RSE) 0.020 0.014
SE.Z:s(RSE) 0.014 0.053 0.492
ggpredict(m, c("SE.Z", "RSE")) %>% plot()
Data were 'prettified'. Consider using `terms="SE.Z [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(SCC) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ SE.Z * scale(SCC) + (SE.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2097.6 2140.7 -1040.8 2081.6 1596
Scaled residuals:
Min 1Q Median 3Q Max
-4.1113 -0.9834 0.5061 0.9048 1.3666
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.01530 0.1237
subID (Intercept) 0.38775 0.6227
SE.Z 0.08051 0.2837 0.75
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.3883397 0.1967117 1.974 0.0484 *
SE.Z 0.1716780 0.1026918 1.672 0.0946 .
scale(SCC) 0.3372548 0.1983321 1.700 0.0890 .
SE.Z:scale(SCC) 0.0002394 0.1042905 0.002 0.9982
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE.Z s(SCC)
SE.Z 0.611
scale(SCC) 0.027 0.017
SE.Z:s(SCC) 0.016 0.054 0.606
ggpredict(m, c("SE.Z", "SCC")) %>% plot()
Data were 'prettified'. Consider using `terms="SE.Z [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(DS) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ SE.Z * scale(DS) + (SE.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2099.4 2142.4 -1041.7 2083.4 1596
Scaled residuals:
Min 1Q Median 3Q Max
-4.0522 -0.9857 0.5130 0.8996 1.3949
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.01487 0.1219
subID (Intercept) 0.40043 0.6328
SE.Z 0.07830 0.2798 0.66
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38580 0.19959 1.933 0.0532 .
SE.Z 0.17169 0.10167 1.689 0.0913 .
scale(DS) -0.30090 0.19883 -1.513 0.1302
SE.Z:scale(DS) -0.04533 0.10075 -0.450 0.6527
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE.Z sc(DS)
SE.Z 0.540
scale(DS) -0.015 -0.010
SE.Z:sc(DS) -0.009 -0.032 0.542
ggpredict(m, c("SE.Z", "DS")) %>% plot()
Data were 'prettified'. Consider using `terms="SE.Z [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(NFC) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ SE.Z * scale(NFC) + (SE.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2101.4 2144.4 -1042.7 2085.4 1596
Scaled residuals:
Min 1Q Median 3Q Max
-4.1259 -0.9855 0.5086 0.9013 1.3777
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.01483 0.1218
subID (Intercept) 0.49188 0.7013
SE.Z 0.07863 0.2804 0.65
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38828 0.21946 1.769 0.0769 .
SE.Z 0.17204 0.10186 1.689 0.0912 .
scale(NFC) -0.10187 0.21805 -0.467 0.6404
SE.Z:scale(NFC) -0.04587 0.10017 -0.458 0.6470
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE.Z s(NFC)
SE.Z 0.535
scale(NFC) -0.009 -0.006
SE.Z:s(NFC) -0.006 -0.022 0.540
ggpredict(m, c("SE.Z", "NFC")) %>% plot()
Data were 'prettified'. Consider using `terms="SE.Z [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(SING.Ind) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ SE.Z * scale(SING.Ind) + (SE.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2100.4 2143.5 -1042.2 2084.4 1596
Scaled residuals:
Min 1Q Median 3Q Max
-4.1434 -0.9862 0.5333 0.9109 1.4058
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.01541 0.1242
subID (Intercept) 0.49920 0.7065
SE.Z 0.07072 0.2659 0.68
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38703 0.22099 1.751 0.0799 .
SE.Z 0.17143 0.09828 1.744 0.0811 .
scale(SING.Ind) 0.04858 0.21944 0.221 0.8248
SE.Z:scale(SING.Ind) 0.10087 0.09616 1.049 0.2942
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE.Z s(SING
SE.Z 0.554
scl(SING.I) 0.004 -0.001
SE.Z:(SING. 0.000 0.002 0.562
ggpredict(m, c("SE.Z", "SING.Ind")) %>% plot()
Data were 'prettified'. Consider using `terms="SE.Z [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(SING.Inter) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ SE.Z * scale(SING.Inter) + (SE.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2099.0 2142.0 -1041.5 2083.0 1596
Scaled residuals:
Min 1Q Median 3Q Max
-4.0963 -0.9847 0.5114 0.9046 1.3709
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.01497 0.1223
subID (Intercept) 0.38660 0.6218
SE.Z 0.07111 0.2667 0.60
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38734 0.19643 1.972 0.0486 *
SE.Z 0.17113 0.09844 1.738 0.0822 .
scale(SING.Inter) -0.33416 0.19710 -1.695 0.0900 .
SE.Z:scale(SING.Inter) -0.09474 0.09924 -0.955 0.3398
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE.Z s(SING
SE.Z 0.484
scl(SING.I) -0.024 -0.014
SE.Z:(SING. -0.013 -0.049 0.486
ggpredict(m, c("SE.Z", "SING.Inter")) %>% plot()
Data were 'prettified'. Consider using `terms="SE.Z [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(Proto) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ SE.Z * scale(Proto) + (SE.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2097.8 2140.9 -1040.9 2081.8 1596
Scaled residuals:
Min 1Q Median 3Q Max
-4.2393 -0.9825 0.4972 0.9025 1.3777
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.01527 0.1236
subID (Intercept) 0.38403 0.6197
SE.Z 0.07835 0.2799 0.69
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.39377 0.19597 2.009 0.0445 *
SE.Z 0.17037 0.10195 1.671 0.0947 .
scale(Proto) -0.37819 0.20439 -1.850 0.0643 .
SE.Z:scale(Proto) -0.03303 0.11144 -0.296 0.7669
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE.Z scl(P)
SE.Z 0.560
scale(Prot) -0.042 -0.024
SE.Z:scl(P) -0.021 -0.086 0.541
ggpredict(m, c("SE.Z", "Proto")) %>% plot()
Data were 'prettified'. Consider using `terms="SE.Z [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(SI) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ SE.Z * scale(SI) + (SE.Z | subID) + (1 | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2093.5 2136.5 -1038.7 2077.5 1596
Scaled residuals:
Min 1Q Median 3Q Max
-4.5357 -0.9836 0.5497 0.8931 1.7206
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.01110 0.1054
subID (Intercept) 0.50315 0.7093
SE.Z 0.04302 0.2074 0.95
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.39170 0.22185 1.766 0.07746 .
SE.Z 0.18399 0.08448 2.178 0.02941 *
scale(SI) -0.05844 0.22055 -0.265 0.79103
SE.Z:scale(SI) -0.21866 0.08254 -2.649 0.00807 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE.Z sc(SI)
SE.Z 0.701
scale(SI) -0.009 -0.010
SE.Z:sc(SI) -0.011 -0.041 0.707
ggpredict(m, c("SE.Z", "SI")) %>% plot()
Data were 'prettified'. Consider using `terms="SE.Z [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(NTB) + ( SE.Z | subID) + ( SE.Z | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ SE.Z * scale(NTB) + (SE.Z | subID) + (SE.Z | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2105.3 2159.1 -1042.6 2085.3 1594
Scaled residuals:
Min 1Q Median 3Q Max
-4.1207 -0.9849 0.5092 0.8972 1.3783
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 1.487e-02 0.121954
SE.Z 2.509e-05 0.005009 -1.00
subID (Intercept) 4.902e-01 0.700153
SE.Z 8.022e-02 0.283237 0.66
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.884e-01 2.191e-01 1.772 0.0763 .
SE.Z 1.717e-01 1.026e-01 1.672 0.0945 .
scale(NTB) -1.108e-01 2.181e-01 -0.508 0.6115
SE.Z:scale(NTB) 8.541e-05 1.016e-01 0.001 0.9993
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SE.Z s(NTB)
SE.Z 0.545
scale(NTB) -0.010 -0.006
SE.Z:s(NTB) -0.006 -0.024 0.551
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("SE.Z", "NTB")) %>% plot()
Data were 'prettified'. Consider using `terms="SE.Z [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(RSE) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(RSE) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2111.4 2165.2 -1045.7 2091.4 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8959 -1.0144 0.4983 0.9101 1.4069
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003365 0.05801
scale(desirability) 0.013385 0.11570 1.00
subID (Intercept) 0.415188 0.64435
scale(desirability) 0.001739 0.04170 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38520 0.20272 1.900 0.05741 .
scale(desirability) 0.20932 0.06083 3.441 0.00058 ***
scale(RSE) 0.27336 0.20314 1.346 0.17840
scale(desirability):scale(RSE) 0.02205 0.05729 0.385 0.70036
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(RSE)
scl(dsrblt) 0.218
scale(RSE) 0.019 0.019
scl():(RSE) 0.018 0.128 0.233
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("desirability", "RSE")) %>% plot()
Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SCC) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(SCC) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2110.0 2163.8 -1045.0 2090.0 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8913 -1.0078 0.4896 0.9083 1.3630
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003354 0.05791
scale(desirability) 0.013479 0.11610 1.00
subID (Intercept) 0.377675 0.61455
scale(desirability) 0.003107 0.05574 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.385673 0.194130 1.987 0.046958 *
scale(desirability) 0.209174 0.061907 3.379 0.000728 ***
scale(SCC) 0.337654 0.195663 1.726 0.084403 .
scale(desirability):scale(SCC) -0.006718 0.060638 -0.111 0.911783
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(SCC)
scl(dsrblt) 0.280
scale(SCC) 0.026 0.024
scl():(SCC) 0.022 0.143 0.293
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("desirability", "SCC")) %>% plot()
Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(DS) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(DS) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2110.9 2164.7 -1045.4 2090.9 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.7639 -1.0088 0.4900 0.9098 1.4151
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0034089 0.05839
scale(desirability) 0.0134907 0.11615 1.00
subID (Intercept) 0.3893895 0.62401
scale(desirability) 0.0009413 0.03068 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38260 0.19680 1.944 0.051891 .
scale(desirability) 0.20787 0.06023 3.451 0.000558 ***
scale(DS) -0.29740 0.19610 -1.517 0.129382
scale(desirability):scale(DS) -0.03279 0.05445 -0.602 0.547033
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) sc(DS)
scl(dsrblt) 0.167
scale(DS) -0.015 -0.017
scl(d):(DS) -0.015 -0.103 0.179
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("desirability", "DS")) %>% plot()
Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(NFC) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(NFC) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2112.6 2166.4 -1046.3 2092.6 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8577 -1.0118 0.4889 0.9094 1.3512
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003355 0.05792
scale(desirability) 0.013450 0.11598 1.00
subID (Intercept) 0.479426 0.69241
scale(desirability) 0.002268 0.04762 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38517 0.21664 1.778 0.075415 .
scale(desirability) 0.20871 0.06126 3.407 0.000657 ***
scale(NFC) -0.09903 0.21536 -0.460 0.645636
scale(desirability):scale(NFC) 0.01942 0.05410 0.359 0.719619
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(NFC)
scl(dsrblt) 0.244
scale(NFC) -0.008 -0.007
scl():(NFC) -0.008 -0.060 0.264
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("desirability", "NFC")) %>% plot()
Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SING.Ind) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(SING.Ind) +
(scale(desirability) | subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2112.7 2166.5 -1046.4 2092.7 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8602 -1.0147 0.4910 0.9059 1.3673
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003337 0.05777
scale(desirability) 0.013431 0.11589 1.00
subID (Intercept) 0.486231 0.69730
scale(desirability) 0.002218 0.04710 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38508 0.21806 1.766 0.077406 .
scale(desirability) 0.20921 0.06118 3.419 0.000628 ***
scale(SING.Ind) 0.05346 0.21663 0.247 0.805083
scale(desirability):scale(SING.Ind) -0.02489 0.05367 -0.464 0.642820
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(SING
scl(dsrblt) 0.242
scl(SING.I) 0.004 0.003
s():(SING.I 0.003 0.022 0.263
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("desirability", "SING.Ind")) %>% plot()
Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SING.Inter) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(SING.Inter) +
(scale(desirability) | subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2110.4 2164.2 -1045.2 2090.4 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.8256 -1.0104 0.4934 0.9125 1.4150
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.0000000 0.00000
scale(desirability) 0.0093185 0.09653 NaN
subID (Intercept) 0.3727206 0.61051
scale(desirability) 0.0009385 0.03063 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38345 0.19280 1.989 0.046716 *
scale(desirability) 0.20642 0.05956 3.466 0.000529 ***
scale(SING.Inter) -0.33236 0.19353 -1.717 0.085910 .
scale(desirability):scale(SING.Inter) -0.03516 0.05701 -0.617 0.537466
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(SING
scl(dsrblt) 0.162
scl(SING.I) -0.022 -0.021
s():(SING.I -0.020 -0.138 0.177
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("desirability", "SING.Inter")) %>% plot()
Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(Proto) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(Proto) +
(scale(desirability) | subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2109.2 2163.0 -1044.6 2089.2 1594
Scaled residuals:
Min 1Q Median 3Q Max
-4.3834 -1.0152 0.5623 0.9062 1.4029
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003279 0.05726
scale(desirability) 0.013172 0.11477 1.00
subID (Intercept) 0.374479 0.61195
scale(desirability) 0.001273 0.03567 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.39167 0.19357 2.023 0.043030 *
scale(desirability) 0.21533 0.06089 3.536 0.000406 ***
scale(Proto) -0.38362 0.20188 -1.900 0.057402 .
scale(desirability):scale(Proto) -0.07237 0.07165 -1.010 0.312464
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) scl(P)
scl(dsrblt) 0.194
scale(Prot) -0.043 -0.047
scl(ds):(P) -0.038 -0.225 0.229
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("desirability", "Proto")) %>% plot()
Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SI) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(SI) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2110.7 2164.5 -1045.4 2090.7 1594
Scaled residuals:
Min 1Q Median 3Q Max
-4.0641 -1.0169 0.5313 0.9074 1.5565
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.003313 0.05756
scale(desirability) 0.013506 0.11621 1.00
subID (Intercept) 0.492358 0.70168
scale(desirability) 0.002593 0.05092 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38629 0.21938 1.761 0.078272 .
scale(desirability) 0.21359 0.06156 3.470 0.000521 ***
scale(SI) -0.05538 0.21816 -0.254 0.799632
scale(desirability):scale(SI) -0.08211 0.05464 -1.503 0.132865
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) sc(SI)
scl(dsrblt) 0.260
scale(SI) -0.008 -0.009
scl(d):(SI) -0.009 -0.079 0.280
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("desirability", "SI")) %>% plot()
Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(NTB) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
boundary (singular) fit: see help('isSingular')
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: as.factor(ingChoiceN) ~ scale(desirability) * scale(NTB) + (scale(desirability) |
subID) + (scale(desirability) | trait)
Data: fullTest
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
AIC BIC logLik deviance df.resid
2112.8 2166.6 -1046.4 2092.8 1594
Scaled residuals:
Min 1Q Median 3Q Max
-3.9039 -1.0131 0.5005 0.9074 1.4211
Random effects:
Groups Name Variance Std.Dev. Corr
trait (Intercept) 0.00337 0.05805
scale(desirability) 0.01338 0.11569 1.00
subID (Intercept) 0.47789 0.69130
scale(desirability) 0.00211 0.04593 1.00
Number of obs: 1604, groups: trait, 148; subID, 11
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38539 0.21633 1.782 0.07483 .
scale(desirability) 0.20947 0.06112 3.427 0.00061 ***
scale(NTB) -0.11353 0.21542 -0.527 0.59819
scale(desirability):scale(NTB) -0.01384 0.05480 -0.253 0.80060
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(d) s(NTB)
scl(dsrblt) 0.237
scale(NTB) -0.010 -0.010
scl():(NTB) -0.009 -0.075 0.255
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
ggpredict(m, c("desirability", "NTB")) %>% plot()
Data were 'prettified'. Consider using `terms="desirability [all]"` to get smooth plots.

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(RSE) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z*as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel","RSE")) %>% plot()
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SCC) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SCC")) %>% plot()
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(DS) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","DS")) %>% plot()
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(NFC) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel", "NFC")) %>% plot()
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SING.Ind) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SING.Ind")) %>% plot()
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SING.Inter) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SING.Inter")) %>% plot()
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(Proto) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "Proto")) %>% plot()
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SI) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SI")) %>% plot()
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(NTB) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 100000)),
nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel", "NTB")) %>% plot()
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(lmerTest)
library(ggeffects)
library(dplyr)
library(report)
library(r2glmm)
```

```{r}
fullTest <- read.csv("../Cleaning/output/fullTest.csv")
fullTest$ingChoiceN <- as.factor(fullTest$ingChoiceN)
fullTest$novel <- as.factor(fullTest$novel)
fullTest$selfResp.Z <- scale(fullTest$selfResp)
fullTest$SE.Z <- scale(fullTest$SE)
fullTest$iSE.Z <- scale(fullTest$iSE)
fullTest$oSE.Z <- scale(fullTest$oSE)

```

# Replication of prior self-anchoring findings: Self-evaluations predicting ingroup evaluations

```{r}
m <- glmer( ingChoiceN ~ selfResp.Z + ( selfResp.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
library(broom.mixed)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
```

```{r}
m <- glmer( ingChoiceN ~ SE.Z + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
```

```{r}
m <- glmer( ingChoiceN ~ SE.Z * novel + ( SE.Z + novel | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
tidy(m,conf.int=TRUE,exponentiate=TRUE,effects="fixed")
r2beta(m)
```


```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z + scale(desirability) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

m <- glmer( as.factor(ingChoiceN) ~ scale(oSE) + ( scale(oSE) | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

m <- glmer( as.factor(ingChoiceN) ~ scale(iSE) + ( scale(iSE) | subID) + (1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel) + ( SE.Z + as.factor(novel) | subID) + (  1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel) + scale(desirability) + ( SE.Z + as.factor(novel) + scale(desirability) | subID) + (  1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(RSE) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE.Z", "RSE")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(SCC) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE.Z", "SCC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(DS) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE.Z", "DS")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(NFC) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE.Z", "NFC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(SING.Ind) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE.Z", "SING.Ind")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(SING.Inter) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE.Z", "SING.Inter")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(Proto) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE.Z", "Proto")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(SI) + ( SE.Z | subID) + ( 1 | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE.Z", "SI")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ SE.Z*scale(NTB) + ( SE.Z | subID) + ( SE.Z | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE.Z", "NTB")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(RSE) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "RSE")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SCC) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "SCC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(DS) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "DS")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(NFC) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "NFC")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SING.Ind) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "SING.Ind")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SING.Inter) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "SING.Inter")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(Proto) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "Proto")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(SI) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "SI")) %>% plot()

m <- glmer( as.factor(ingChoiceN) ~ scale(desirability)*scale(NTB) + ( scale(desirability) | subID) + ( scale(desirability) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("desirability", "NTB")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(RSE) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z*as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel","RSE")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SCC) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SCC")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(DS) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","DS")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(NFC) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel", "NFC")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SING.Ind) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SING.Ind")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SING.Inter) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SING.Inter")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(Proto) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "Proto")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(SI) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE","novel","SI")) %>% plot()
```

```{r}
m <- glmer( as.factor(ingChoiceN) ~ SE.Z*as.factor(novel)*scale(NTB) + ( SE.Z+as.factor(novel) | subID) + ( SE.Z+as.factor(novel) | trait), data = fullTest, family = binomial, control = glmerControl(optimizer = "bobyqa",
                                    optCtrl = list(maxfun = 100000)),
    nAGQ = 1)
summary(m)
ggpredict(m, c("SE", "novel", "NTB")) %>% plot()
```


